Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148250/1/rssc12320.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148250/2/rssc12320_am.pd
A Bayesian spatial model for detecting brain activation in functional neuroimaging (here focusing on...
Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permit...
In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is propose...
University of Minnesota Ph.D. dissertation. January 2015. Major: Statistics. Advisors: Galin Jones a...
We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), in...
models for functional magnetic resonance imaging data analysis Linlin Zhang,1 Michele Guindani2 and ...
In this research work, I propose Bayesian nonparametric approaches to model functional magnetic reso...
We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of G...
University of Minnesota Ph.D. dissertation. August 2010. Major: Statistics. Advisor: Jones, Galin. 1...
Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an ind...
We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise inn...
Magnetic Resonance Imaging (MRI) is a foundational tool for medical and academic research. Functiona...
Functional neuroimaging techniques enable investigations into the neural basis of human cognition, e...
Human brain mapping, i.e. the detection of functional regions and their connections, has experienced...
In this paper we propose a procedure to undertake Bayesian variable selection and model averaging fo...
A Bayesian spatial model for detecting brain activation in functional neuroimaging (here focusing on...
Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permit...
In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is propose...
University of Minnesota Ph.D. dissertation. January 2015. Major: Statistics. Advisors: Galin Jones a...
We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), in...
models for functional magnetic resonance imaging data analysis Linlin Zhang,1 Michele Guindani2 and ...
In this research work, I propose Bayesian nonparametric approaches to model functional magnetic reso...
We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of G...
University of Minnesota Ph.D. dissertation. August 2010. Major: Statistics. Advisor: Jones, Galin. 1...
Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an ind...
We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise inn...
Magnetic Resonance Imaging (MRI) is a foundational tool for medical and academic research. Functiona...
Functional neuroimaging techniques enable investigations into the neural basis of human cognition, e...
Human brain mapping, i.e. the detection of functional regions and their connections, has experienced...
In this paper we propose a procedure to undertake Bayesian variable selection and model averaging fo...
A Bayesian spatial model for detecting brain activation in functional neuroimaging (here focusing on...
Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permit...
In this work an Empirical Markov Chain Monte Carlo Bayesian approach to analyse fMRI data is propose...
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